Self-Driving Car A Deep-Learning Approach

Nowadays self-directed learning and automation are not restricted to human beings only. If you stare out at the automotive horizon, you can see a new exciting era coming into limelight: the age of self-driving cars. An age when humans will no longer need to keep their eyes on the road. No more concerns about distraction while driving or those stressful rush hour commutes, vehicles will whisk us where we want to go, blazingly fast and efficiently. This paper aims at demonstrating a system, which is able to drive a car on road without any human input. Both software and hardware parts are discussed here. The vehicle would contain certain sensors such as GPS, Ultrasonic Sensor, Camera and would contain an on-board computer for decision making. Waypoint data would be obtained from a nav provider like Google Maps. All of it would be simulated in CARLA, an open-source simulator

A study of clinicoradiological and functional outcomes of intramedullary nailing in diaphyseal radius ulna fractures

Background: The aim of this study was to evaluate the results of intramedullary nailing in diaphyseal fractures of radius and ulna in age group of 10 to 49 years and to understand its clinicoradiological and functional results.Methods: This is a retrospective case series study of forearm bone fractures and the selected management for the same over a period of 3 years. We chose the cases in which intramedullary nailing was the treatment modality which were followed up over a period of minimum 6 months. Patients with galeazzi variety, monteggia variety, pathological fracture or non-union after previous surgery were excluded. The outcomes were then evaluated with disabilities of the arm, shoulder and hand (DASH) score, Green and O’Brien score, and Grace and Eversmann functional outcome score.Results: Of the 22 patients, 10 patients had excellent functional outcome according to Grace and Eversmann score, 7 patients had good outcome, 4 patients had acceptable while 1 was unacceptable. Green and O’Brien also had similar results, except that patients among fair category were 3 and poor category were 3. The mean DASH score was 16.2.Conclusions: This study shows that closed method for fixation by intramedullary nailing of both bone forearm fractures leads to excellent to good functional outcomes (according to DASH score, Green and O Brien, and Grace and Eversmann score) with less complications. In 6 months follow up x ray there is radiological union in all cases with no angulation, malunion or non-union.

Personalized machine learning of depressed mood using wearables.

Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and precision therapeutics, new personalized treatment approaches are warranted for depression. Here, we use a combination of longitudinal ecological momentary assessments of depression, neurocognitive sampling synchronized with electroencephalography, and lifestyle data from wearables to generate individualized predictions of depressed mood over a 1-month time period. This study, thus, develops a systematic pipeline for N-of-1 personalized modeling of depression using multiple modalities of data. In the models, we integrate seven types of supervised machine learning (ML) approaches for each individual, including ensemble learning and regression-based methods. All models were verified using fourfold nested cross-validation. The best-fit as benchmarked by the lowest mean absolute percentage error, was obtained by a different type of ML model for each individual, demonstrating that there is no one-size-fits-all strategy. The voting regressor, which is a composite strategy across ML models, was best performing on-average across subjects. However, the individually selected best-fit models still showed significantly less error than the voting regressor performance across subjects. For each individual's best-fit personalized model, we further extracted top-feature predictors using Shapley statistics. Shapley values revealed distinct feature determinants of depression over time for each person ranging from co-morbid anxiety, to physical exercise, diet, momentary stress and breathing performance, sleep times, and neurocognition. In future, these personalized features can serve as targets for a personalized ML-guided, multimodal treatment strategy for depression

Impact of Childhood Trauma on Executive Function in Adolescence—Mediating Functional Brain Networks and Prediction of High-Risk Drinking

BackgroundChildhood trauma is known to impart risk for several adverse life outcomes. Yet, its impact during adolescent development is not well understood. We aimed to investigate the relationships among childhood trauma, functional brain connectivity, executive dysfunction (ED), and the development of high-risk drinking in adolescence.MethodsData from the National Consortium on Alcohol and Neurodevelopment in Adolescence (sample size = 392, 55% female) cohort were used. This included resting-state functional magnetic resonance imaging at baseline, childhood trauma and ED self-reports, and detailed interviews on alcohol and substance use collected at baseline and at 4 annual follow-ups. We used longitudinal regression analyses to confirm the relationship between childhood trauma and ED, identified the mediating functional brain network hubs, and used these linkages to predict future high-risk drinking in adolescence.ResultsChildhood trauma severity was significantly related to ED in all years. At baseline, distributed functional connectivity from hub regions in the bilateral dorsal anterior cingulate cortex, right anterior insula, right intraparietal sulcus, and bilateral pre- and postcentral gyri mediated the relationship between childhood trauma and ED. Furthermore, high-risk drinking in follow-up years 1-4 could be predicted with high accuracy from the trauma-affected functional brain networks that mediated ED at baseline, together with age, childhood trauma severity, and extent of ED.DiscussionFunctional brain networks, particularly from hub regions important for cognitive and sensorimotor control, explain the relationship between childhood trauma and ED and are important for predicting future high-risk drinking. These findings are relevant for the prognosis of alcohol use disorders